Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Information storage and retrieval
Information storage and retrieval
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WebACE: a Web agent for document categorization and exploration
AGENTS '98 Proceedings of the second international conference on Autonomous agents
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An adaptive algorithm for learning changes in user interests
Proceedings of the eighth international conference on Information and knowledge management
Learning users' interests by unobtrusively observing their normal behavior
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Automatic personalization based on Web usage mining
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SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
ACIRD: Intelligent Internet Document Organization and Retrieval
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Ontology Based Personalized Search
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Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
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DS'07 Proceedings of the 10th international conference on Discovery science
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CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Collaborative user modeling for enhanced content filtering in recommender systems
Decision Support Systems
An effective recommendation method for cold start new users using trust and distrust networks
Information Sciences: an International Journal
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In this paper, we present PVA, an adaptive personal view information agent system for tracking, learning and managing user interests in Internet documents. PVA consists of three parts: a proxy, personal view constructor, and personal view maintainer. The proxy logs the user's activities and extracts the user's interests without user intervention. The personal view constructor mines user interests and maps them to a class hierarchy (i.e., personal view). The personal view maintainer synchronizes user interests and the personal view periodically. When user interests change, in PVA, not only the contents, but also the structure of the user profile are modified to adapt to the changes. In addition, PVA considers the aging problem of user interests. The experimental results show that modulating the structure of the user profile increases the accuracy of a personalization system.